Authors:
Yasuo Saito
;
Masaomi Kimura
and
Satoshi Ishizaki
Affiliation:
Shibaura Institute of Technology, Japan
Keyword(s):
Soccer, Sports Data, Game Prediction, k-NN, Clustering.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Clustering and Classification Methods
;
Data Analytics
;
Data Engineering
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Data analysis in sports has been developing for many years. However, to date, a system that provides tactical
prediction in real time and promotes ideas for increasing the chance of winning has not been reported in the
literature. Especially, in soccer, components of plays and games are more complicated than in other sports.
This study proposes a method to predict the course of a game and create a strategy for the second half. First, we
summarize other studies and propose our method. Then, data are collected using the proposed system. From
past games, games to similar to a target game are extracted depending on data from their first half. Next, similar
games are classified by features depending on data of their second half. Finally, a target game is predicted and
tactical ideas are derived. The practicability of the method is demonstrated through experiments. However,
further improvements such as increasing the number of past games and types of data are still required.